Abstract
In order to accurately grasp the influencing factors of consumer product satisfaction with new energy vehicles, this paper uses scenario-based thinking as the framework and a large amount of multi-source heterogeneous data as the basis. Dimensional matching is performed on the structured data, and the random forest machine learning method is used to obtain complete sample data to reflect the overall market characteristics; thus, the CatBoost model is trained based on the complete sample data, and the degree of influence of each feature on satisfaction is analyzed. Through research and analysis, it is found that not only the performance of the product itself has a greater impact on product satisfaction, but the characteristics of consumers’ own attributes are also an important factor affecting product satisfaction. Based on the research conclusions of this article, it can provide reference and basis for the improvement of enterprise product satisfaction and precision marketing.
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